Image processing apparatus and method

ABSTRACT

The disclosure provides an image processing apparatus and method. The image processing apparatus may include: a filter for performing an iterative filtering on an input image; and an iterative filtering stopping device for determining whether to stop the iterative filtering according to a variation speed of the filtered image obtained from each iteration of the iterative filtering with increasing of iteration times.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromChinese Patent Application No. 201110045243.3, filed on Feb. 22, 2011,the entire contents of which are incorporated herein by reference.

FIELD

The disclosure relates to the field of image processing, and moreparticularly to image processing apparatus and method for the noisereduction of an image (e.g. a medical image).

BACKGROUND

Image noise reduction is an important link in image processing. Forexample, during the processing of a medical image, due to the problemssuch as inadequate signal detection performance and imaging algorithm ofan apparatus, low dosage of scanning agent and the like, noise isgenerally contained in a medical image obtained by a computed tomography(CT) apparatus, a magnetic resonance imaging (MRI) apparatus, anultrasound (UL) diagnostic apparatus and other medical apparatuses. Thenoise in such images will cause a severe influence on the diagnosis of adoctor as well as an influence on the accuracy of the subsequent imageprocessing (e.g. image segmentation, image measurement and volumerending).

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned and other objects, features and advantages of thedisclosure will be more readily understood by reference to thedescription on the following embodiments of the disclosure whenconsidered in connection with the appended drawings. The componentsshown in the appended drawings are only for illustrating the principleof the disclosure, and in the appended drawings, identical or similartechnical features or components are represented with identical orsimilar appended drawing reference signs.

FIG. 1 is a block diagram illustrating the structure of an imageprocessing apparatus according to an embodiment of the disclosure;

FIG. 2 is a schematic diagram illustrating the flow of an imageprocessing method according to the embodiment of FIG. 1;

FIG. 3 is a block diagram illustrating the structure of an imageprocessing apparatus according to a specific embodiment of thedisclosure;

FIG. 4 is a schematic diagram illustrating the flow of an imageprocessing method according to the specific embodiment of FIG. 3;

FIG. 5 is a schematic diagram illustrating the flow of a specificexample of determining an iterative filtering stopping time;

FIGS. 6A, 6B and 6C are schematic diagrams which respectively illustratethe curves reflecting the variation of the correlation coefficientbetween a filtered image and a noise image during the iterativefiltering process of different types of images;

FIG. 7 is a schematic diagram illustrating an exemplary applicationscenario of an embodiment of the disclosure;

FIG. 8 is a block diagram illustrating the structure of an imageprocessing apparatus according to another specific embodiment of thedisclosure;

FIG. 9 is a block diagram illustrating the structure of an imageprocessing apparatus according to another specific embodiment of thedisclosure;

FIG. 10 is a schematic diagram illustrating the flow of the imageprocessing method shown in the embodiment of FIG. 8;

FIG. 11 is a schematic diagram illustrating the flow of the imageprocessing method shown in the embodiment of FIG. 9;

FIGS. 12A and 12B are schematic diagrams which respectively illustratethe curves reflecting the variations of the variances of a filteredimage and a noise image during an iterative filtering process;

FIG. 13 is a block diagram illustrating the structure of an imageprocessing apparatus according to another embodiment of the disclosure;

FIG. 14 is a schematic diagram showing the flow of an image processingmethod according to the specific embodiment of FIG. 13;

FIG. 15 is a schematic diagram showing a specific example of thespecific embodiment of FIG. 13;

FIG. 16 is a block diagram illustrating the structure of an imageprocessing apparatus according to another embodiment of the disclosure;

FIG. 17 is a schematic diagram illustrating the flow of an imageprocessing method according to the embodiment of FIG. 16;

FIG. 18 is a schematic diagram illustrating an exemplary gradientdistribution of each image unit in an image; and

FIG. 19 is a block diagram illustrating the structure of a computer forrealizing the image processing method of the disclosure.

DETAILED DESCRIPTION

Some aspects of the disclosure are described below in brief for a basicunderstanding of the disclosure. The description is merely intended todefine, in a simplified way, some concepts as the preamble of thefollowing detailed description, but not to determine the key orimportant parts of the disclosure or limit the scope of the disclosure.

According to an aspect of the disclosure, there is provided an imageprocessing apparatus which may include: a filter for performing aniterative filtering on an input image; and an iterative filteringstopping device for determining whether to stop the iterative filteringaccording to the variation speed of the filtered image obtained fromeach iteration of the iterative filtering with the increasing ofiteration times.

According to another aspect of the disclosure, there is provided animage processing method which may include: performing an iterativefiltering on an input image; and determining whether to stop theiterative filtering according to the variation speed of the filteredimage obtained from each iteration of the iterative filtering with theincreasing of iteration times.

Furthermore, an embodiment of the disclosure further provides a computerprogram for realizing the image processing method.

Additionally, an embodiment of the disclosure further provides acomputer program product in the form of a medium at least readable to acomputer, on which computer program codes for realizing the imageprocessing method are recorded.

Preferred embodiments of the disclosure are described below withreference to appended drawings. The elements and features described inone of the appended drawings or implementation modes of the disclosuremay be combined with those shown in one or more other appended drawingsor implementation modes. It should be noted that for the sake ofclarity, the representation and description of the components andprocessing that are irrelative with this invention but well known bythose skilled in this art are omitted.

In image processing, an image may be subjected to an iterative filteringbefore being processed so as to filter out as much noise from the imageas possible. Noise reduction may not achieve an ideal effect if fewtimes of iteration are performed, on the other hand, however, if too maytimes of iteration are performed, the useful information in the imagewill be smoothed, leading to the blurring of the image which may haveadverse influence on the subsequent processing. Some embodiments of thedisclosure provide an image processing apparatus and method capable ofdetermining an appropriate iterative filtering stopping time during aniterative filtering process.

It should be appreciated that the apparatus and method provided in theembodiments of the disclosure are applicable to noise reduction onvarious types of images, for example, a medical image formed by the dataobtained by a medical diagnostic imaging device for a person subjectedto physical examination. The medical diagnostic apparatus hereinincludes but is not limited to: X-ray imaging diagnostic apparatus,ultrasound (UL) imaging diagnostic apparatus, computed tomography (CT)apparatus, magnetic resonance imaging (MRI) diagnostic apparatus,positron emission tomography (PET) apparatus and the like. FIG. 7 is aschematic diagram illustrating the acquisition of a medical image by amedical diagnostic apparatus. As shown in FIG. 7, there are providedseveral predetermined or user-defined scanning protocols for each typeof imaging apparatus (e.g. the aforementioned medical diagnosticapparatuses). Taking CT as an example, it may be provided with cardiacsynchronization scanning protocol, abdominal 4-phase enhance scanningprotocol, dynamic scanning protocol and the like, referring to theprotocols 1, 2 . . . N (N≧1) shown in FIG. 7; and a plurality of imagesequences (for example, the sequences 1, 2 . . . N shown in FIG. 7) canbe obtained by using one of such protocols. Therefore, the image to beprocessed may be a medical image that is formed by the patient dataobtained by a medical diagnostic apparatus based on a correspondingprotocol.

FIG. 1 is a block diagram illustrating the structure of an imageprocessing apparatus according to an embodiment of the disclosure, andFIG. 2 is a schematic diagram illustrating the flow of an imageprocessing method according to the embodiment of the FIG. 1.

As shown in FIG. 1, the image processing apparatus 100 may include afilter 101 and an iterative filtering stopping device 103. The imageprocessing apparatus 100 can perform an image filtering by using themethod shown in FIG. 2. And the functions of each device of the imageprocessing apparatus 100 are described below with reference to the flowof the method shown in FIG. 2.

The filter 101 is configured to perform an iterative filtering on aninput image input to the image processing apparatus (Step 2-2 shown inFIG. 2). The filter 101 may use any appropriate filtering methodselected based on an actual application scenario, such as non-lineardiffusion filtering (NLD) method, linear filtering method, anisotropicdiffusion (ATD) method and the like, which should not be limited to anyparticular filtering method here.

The filter 101 outputs the filtered image obtained from each iterationto the iterative filtering stopping device 103, The iterative filteringstopping device 103 is configured to determine whether to stop theiterative filtering according to the filtered image obtained from eachiteration. Specifically, the iterative filtering stopping device 103 maydetermine whether to stop the iterative filtering according to thevariation speed (in the disclosure the term “speed” refers to themagnitude of velocity) of the filtered image with the increase of theiteration times during the filtering process (Step 206 shown in FIG. 2).For instance, if the iterative filtering stopping device 103 determinesthat the variation speed of the filtered image drops to a predeterminedthreshold or even lower, which means that at this moment the filteringoperation causes little influence on the image, in other words, there islittle noise to be filtered out, then the iterative filtering stoppingdevice indicates the filter 101 to stop the iterative filtering,otherwise, the iterative filtering stopping device indicates the filter101 to perform the next iteration.

The variation of the filtered image herein refers to the differencebetween the filtered image obtained from the current filtering iterationand that obtained from a previous filtering iteration (a previousfiltering iteration refers to a filtering iteration performed before thecurrent iteration, e.g. the iteration immediately before the currentiteration, and should not be limited any particular former iteration).During the iterative filtering process, the variation (difference)between the filtered image obtained from each iteration and thatobtained from the previous iteration is decreased with the increase ofiteration times. That is, the variation speed of the filtered imagedecreases with the increase of the iteration times. The apparatus andmethod shown in FIG. 1 and FIG. 2 determine, based on this principle,the iterative filtering stopping time according to the variation speedof the filtered image. By adopting the method and apparatus provided inthis disclosure, an appropriate iterative filtering stopping time can beeffectively determined, thereby most noise of the filtered image may befilter out while useful information in the image may be prevented frombeing blurred. In addition, the trouble of manually setting theiterative filtering stopping time may be avoided, which improves theautomation of the image processing.

The variation of the filtered image during the iteration process may bereflected in different characteristics or features of the filteredimage, such as (not limited to) the variation of the correlation betweenthe filtered image and the noise image obtained from each iterationduring the iteration process, the variation of an image feature (e.g.the variance or standard deviation) value of the filtered image or thenoise image and the like, or any mathematical combination of thevariations mentioned above. Several specific embodiments of the imageprocessing apparatus and method for determining whether to stop aniterative filtering according to the variation speed of a filtered imageduring the iterative filtering process are described below withreference to the FIGS. 3-15.

FIG. 3 is a block diagram illustrating the structure of an imageprocessing apparatus according to a specific embodiment of thedisclosure, and FIG. 4 is a schematic diagram illustrating the flow ofan image processing method according to the specific embodiment of theFIG. 3.

As mentioned above, during the iterative filtering process, comparingthe filtered image obtained from each iteration with that obtained fromthe previous iteration, the variation therebetween decreases with theincreasing of the times of the iterations, that is, the variation speedof the filtered image decreases during the iterative filtering. At theinitial stage of the iterative filtering, much noise is filtered out byeach iteration, and with the increase of iteration times, the noise inthe image becomes less and less, and the filtered image itself issmoothed gradually. The variation speed of the filtered image isproportional to the gradient of the filtered image. The gradient of thefiltered image decreases as the filtered image is gradually smoothed,and thus the variation speed of the filtered image declines.Accordingly, since the noise remained in the filtered image decreasesduring the iterations, the correlation between the filtered image andthe noise image varies. In other words, the variation of the correlationalso reflects the variation of the filtered image, and the variationspeed of the correlation reflects the variation speed of the filteredimage. FIGS. 6A, 6B and 6C respectively illustrate the exemplary curvesreflecting the variation of the correlation during the iterativefiltering process. The curves in FIGS. 6A, 6B and 6C respectivelyrepresent the variation trend of the correlation coefficient between thefiltered image obtained from each iteration and the corresponding noiseimage against the time (iteration times) during the iterative filteringprocesses of three different types of images (in the examples shown inFIGS. 6A, 6B and 6C, the three types of images are medical imagesrespectively obtained by an UL, a CT and an MRI apparatus from differentparts of a human body). As shown in FIGS. 6A and 6B, the correlationcoefficient between the filtered image and the noise image shows a trendof decline at the initial stage of the iterative filtering, whichindicates that more and more noise has been filtered out. Then after aperiod of time, the curve of the correlation coefficient drops to thetroughs (e.g. the troughs T1 and T2 that are represented by ‘x’ in thefigures). Then, the correlation coefficient between the filtered and thenoise image is in a slowly-rising trend again, which indicates thatafter the troughs useful information in the filtered image is graduallysmoothed with the increasing of iteration times, that is, some of theuseful information is filtered out (or some useful information isfiltered out so as to be included in the noise image). Accordingly, thecorrelation between the filtered image and the noise image is increasesafter the trough. An existing method determines the iterative filteringstopping time by searching the troughs. In the existing method, the timepoint at which the correlation coefficient between the filtered imageand the noise image is minimal is determined to be the iterativefiltering stopping time point. Reference may be made to the followingdocument: Pavel Mrázek, ‘Selection of Optimal Stopping time forNonlinear Diffusion filtering’ (‘Scale-Space and Morphology in ComputerVision’, Third International Conference, Scale-Space 2001) (hereinafterreferred to as related document 1). In the example shown in FIG. 6A,there is one trough T1, the position of which corresponds to the correctstopping time, and thus in this example the correct stopping time can bedetermined by the existing method. However, this existing method is notapplicable to the examples shown in FIGS. 6B and 6C. For example, in thecurve shown in FIG. 6B, the trough T2 is later than the correct stoppingtime, and after the trough T2 the curve of the correlation coefficientis steady (namely, the value of the correlation coefficient is almostunaltered after the trough T2). As a result, in this case the time pointdetermined by this existing method is the trough T2 rather than thecorrect stopping time. For another example, the curve shown in FIG. 6Cdescends monotonically and there thus exists no trough. Accordingly, inthis case no time point corresponding to the minimal correlationcoefficient can be found by using the existing method, so that thecorrect stopping time can not be determined.

The applicant of the disclosure found that, the three curves in FIGS.6A, 6B and 6C each descend monotonically at the initial stage of theiterative filtering process and the decline speed becomes slower andslower at this initial stage, no matter there exists a trough or not, asshown in FIGS. 6A, 6B and 6C. In the apparatus and method shown in FIG.3 and FIG. 4, the iterative filtering stopping time is determined usingthe decreasing speed (not the time point at which the correlation islowest) of the correlation between the filtered and the noise imageduring the iterative filtering process.

As shown in FIG. 3, an image processing apparatus 300 may include afilter 301 and an iterative filtering stopping device 303. The iterativefiltering stopping device 303 may include a noise image acquisition unit303-1, a correlation acquisition unit 303-2 and a stopping determinationunit 303-3. The image processing apparatus 300 may perform an imagefiltering by using the method shown in FIG. 4. And the functions of eachdevice of the image processing apparatus 300 are described below byreference to the flow of the method shown in FIG. 4.

Similar to the filter 101 shown in FIG. 1, the filter 301 is configuredto perform an iterative filtering on an input image input to the imageprocessing apparatus (Step 402 shown in FIG. 4) by utilizing anyappropriate method, the description of which is not repeated here.

The filter 301 outputs the filtered image obtained from each iterationto the noise image acquisition unit 303-1. The noise image acquisitionunit 303-1 is configured to evaluate, after each iteration, the noiseimage according to the obtained filtered image and the input image (Step406-1 shown in FIG. 4).

As an example, the difference image between the filtered image obtainedfrom each iteration and the input image can be calculated and utilizedas the corresponding noise image. Supposing I_(o) represents the inputimage and I_(st) represents the filtered image obtained from the currentfiltering iteration, then the corresponding noise image I_(nt) can beestimated according to the following formula:

I _(nt) =I _(o) −I _(st)  (1)

It should be appreciated that, the noise image after each iteration canbe estimated by any other appropriate method including utilizing anymathematical variations of formula (1). For example, the absolute valueof the difference image calculated in accordance with formula (1) may beused, that is, I_(nt)=|I_(o)−I_(st)|. Other appropriate estimationmethods are not enumerated here. The disclosure is not limited to theparticular examples.

The noise image acquisition unit 303-1 outputs the obtained noise imageto the correlation acquisition unit 303-2. The correlation acquisitionunit 303-2 may estimate the correlation between the noise image and thefiltered image obtained from the current iteration (Step 406-2 shown inFIG. 4) and output the result of the estimation to the stoppingdetermination unit 303-3.

As an example, the correlation coefficient between the noise image andthe filtered image can be calculated to represent the correlationbetween the two images. For example, the correlation between the twoimages can be calculated according to the following formula:

$\begin{matrix}{\rho_{t} = \frac{E\left\{ {\left\lbrack {I_{nt} - {E\left( I_{nt} \right)}} \right\rbrack \left\lbrack {I_{st} - {E\left( I_{st} \right)}} \right\rbrack} \right\}}{\sqrt{{E\left\lbrack {I_{nt} - {E\left( I_{nt} \right)}} \right\rbrack}^{2}} \cdot \sqrt{{E\left\lbrack {I_{st} - {E\left( I_{st} \right)}} \right\rbrack}^{2}}}} & (2)\end{matrix}$

wherein, ρ_(t) represents the correlation coefficient between thefiltered image obtained from the current iteration and the correspondingnoise image I_(nt), and E() represents the mean value function.

It should be appreciated that in addition to the correlation coefficientmentioned above, the correlation between the two images can berepresented by other parameters, including any form of mathematicalvariations of formula (2), and the disclosure is not limited to thisexample.

The stopping determination unit 303-3 is configured to determine whetherto stop the iterative filtering according to the decreasing speed of thecorrelation between the filtered image obtained from each iteration andthe noise image (Step 406-3 shown in FIG. 4). If determining thedecreasing speed of the correlation is smaller than or equal to apredetermined value, the stopping determination unit 303-3 indicates thefilter 301 to stop the iterative filtering, otherwise, indicates thefilter 301 to perform the next iteration.

FIG. 5 shows an example of determining the iterative stopping timeaccording to a feature value representing the decreasing speed of thecorrelation. As shown in FIG. 5, in Step 406-31 the stoppingdetermination unit 303-3 calculates a feature value, which representsthe decreasing speed of the correlation with the increase of iterationtimes, according to the value of the correlation between the filteredimage and the noise image obtained from the current iteration and thevalue of the correlation estimated in the previous iteration. Then, inStep 406-32, the stopping determination unit 303-3 determines whether apredetermined condition between the feature value and a predeterminedthreshold is met, and if yes, stops the iterative filtering, otherwise,continues to perform the next iteration.

As a specific example, the feature value representing the decreasingspeed of the correlation may be the inclination of the curve (thecorrelation coefficient curve shown in FIG. 6A, 6B or 6C) representingthe variation of the correlation between the filtered image and thenoise image with iteration times. For example, the inclination θ may becalculated according to the following formula:

$\begin{matrix}{\theta = {\arctan {\frac{\rho_{t} - \rho_{t - {\Delta \; t}}}{\Delta \; t} \cdot \frac{180}{\pi}}}} & (3)\end{matrix}$

wherein ρ_(t) represents the correlation coefficient between thefiltered image I_(st) and the corresponding noise image I_(nt) obtainedat time t, ρ_(t−Δt) represents the correlation coefficient between thefiltered image I_(s(t−Δt)) and the corresponding noise image I_(n(t−Δt))obtained at time t−Δt. Δt represents a time interval.

As another specific example, the inclination may be represented byradian of the curve:

$\begin{matrix}{\theta_{rad} = {\arctan \frac{\rho_{t} - \rho_{t - {\Delta \; t}}}{\Delta \; t}}} & (4)\end{matrix}$

If determining the calculated inclination θ_(rad) or θ is smaller thanor equal to a predetermined threshold, the stopping determination unit303-3 indicates the filter 301 to stop the iterative filtering,otherwise, indicates the filter 301 to perform the next iteration.

As another specific example, the feature value may be the slope of thecurve (the correlation coefficient curve shown in FIG. 6A, 6B or 6C)representing the variation of the correlation between the filtered imageand the noise image with the increase of the iteration times. Forexample, the slope k_(slope) may be calculated according to thefollowing formula:

$\begin{matrix}{k_{slope} = \frac{\rho_{t} - \rho_{t - {\Delta \; t}}}{\Delta \; t}} & (5)\end{matrix}$

wherein ρ_(t) represents the correlation coefficient between thefiltered image I_(st) and the corresponding noise image I_(nt) obtainedat time T, ρ_(t−Δt) represents the correlation coefficient between thefiltered image and the corresponding noise image I_(s(t−Δt)) obtained attime t-Δt. Δt represents a time interval.

If determining the calculated slope k_(slope) is smaller than or equalto a predetermined threshold, the stopping determination unit 303-3indicates the filter 301 to stop the iterative filtering, otherwise,indicates the filter 301 to perform the next iteration.

It should be noted that the feature value representing the decreasingspeed of the correlation between the filtered image and the noise imageduring the iterative filtering process may be any other feature valuethat may represent the decreasing speed of the correlation, and shouldnot be limited to the inclination and the slope of the correlationcoefficient curve above. For instance, the difference (or the reciprocalof the difference) between the correlation coefficient ρ_(t) estimatedafter the current iteration and the correlation coefficient ρ_(t−Δt)estimated after the previous iteration can be used as the feature value;the iterative filtering is stopped if the difference is determined to besmaller than or equal to a predetermined threshold (or the reciprocal ofthe difference is determined to be greater than or equal to apredetermined threshold), otherwise, the next iteration is performed.Moreover, the aforementioned feature values may be calculated by usingany forms of mathematical variations thereof.

Additionally, it should be appreciated that the predetermined thresholdmentioned in the embodiment/example above may be predetermined accordingto an actual application scenario, for example, it can be anexperimental value or empirical value resulting from an analysis onimages of different types (or features). Different threshold values maybe used in different application scenarios or when different featurevalue parameters are adopted. For instance, in the case where the inputimage is a medical image formed by the data obtained by a medicaldiagnostic apparatus, a corresponding threshold can be predetermined foreach type of scanning protocol or imaging device based on experiments orexperience. Optionally, a plurality of thresholds (for example, in theform of a configuration file) for a plurality of scanning protocols orimaging devices may be stored in a storage unit (built in the imageprocessing apparatus or located outside and accessible to the imageprocessing apparatus). In this way, a corresponding threshold can beacquired (for example, read from the storage unit) during the iterativefiltering process according to the type of the scanning protocol of theinput image or the type of an imaging device to serve as the basis fordetermining the decreasing speed of the correlation. In this way theautomation of image processing may be improved.

In the image processing apparatus or method illustrated in FIGS. 3-5, byusing the decreasing speed of the correlation between the filtered imageand the noise image, the iterative filtering stopping time can beeffectively determined, so that the useful information in the filteredimage is guaranteed not to be smoothed while most noise is filtered outfrom the filtered image.

FIGS. 8 and 9 are block diagrams which respectively illustrate thestructure of an image processing apparatus according to two otherspecific embodiments of the disclosure, and FIGS. 10 and 11 areschematic diagrams which respectively illustrate the flow of an imageprocessing method according to the two specific embodiments. In theembodiments illustrated in FIGS. 3-5, the variation speed of thefiltered image during the iterative filtering process is represented bythe decreasing speed of the correlation between the filtered image andthe noise image. In the embodiments shown in FIGS. 8-11, the variationspeed of the filtered image is represented by the variation speed of apredetermined image feature value of the filtered image or noise imageduring the iterative filtering process.

The predetermined image feature may be any image feature that reflectsthe variation of the filtered image during the iterative filteringprocess, such as the variance or standard deviation of the filteredimage or noise image, the average gradient of the pixels of the filteredimage or noise image, the dynamic range of the gray levels (namely, thedifference between the maximum gray level and the minimal gray level) ofthe filtered image or noise image, or any other features that are notenumerated here.

FIGS. 12A and 12B respectively illustrate exemplary variation curves ofthe variances of the filtered image and a noise image during aniterative filtering process. As shown in FIG. 12A, with the increase oftimes of iterations, the variance of the filtered image descendsmonotonously and the descending speed thereof is lowered gradually. Asshown in FIG. 12B, with the increase of iteration times, the variance ofthe noise image ascends monotonously and the ascending speed thereof islowered gradually. The noise image can be estimated by theabove-mentioned method, the description of which is not repeated herein.

An embodiment of determining a filtering stopping time using thevariation speed of a predetermined image feature value of the filteredimage is described below by reference to FIGS. 8 and 10.

As shown in FIG. 8, an image processing apparatus 800 may include afilter 801 and an iterative filtering stopping device 803. The iterativefiltering stopping device 803 may include an image feature calculationunit 803-4 and a stopping determination unit 803-3. The image processingapparatus 800 may perform an image filtering by using the method shownin FIG. 10.

Similar to the filters 101 and 301, the filter 801 is configured toperform an iterative filtering on an image input to the image processingapparatus (Step 1002 shown in FIG. 10), the description of which is notrepeated here.

The filter 801 outputs the filtered image obtained from each iterationto the image feature calculation unit 803-4. The image featurecalculation unit 803-4 calculates a predetermined image feature value,such as the variance or standard deviation, of the filtered image (Step1006-1 shown in FIG. 10) and outputs the result of the calculation tothe stopping determination unit 803-3. The stopping determination unit803-3 determines whether to stop the iterative filtering according tothe variation speed (e.g. the decreasing speed of the variance orstandard deviation) of the image feature value (e.g. variance orstandard deviation) with the increase of iteration times during theiterative filtering process. If determining the variation speed of theimage feature value is smaller than or equal to a predetermined value,the stopping determination unit 803-3 indicates the filter 801 to stopthe iterative filtering, otherwise, indicates the filter 801 to performthe next iteration.

As a specific example, the stopping determination unit 803-3 maycalculate a feature value representing the variation speed of thepredetermined image feature value during the iterative filtering processaccording to the calculation result of the image feature calculationunit 803-4. For example, the feature value may be the inclination orslope of a curve reflecting the variation of a predetermined imagefeature value with the increase of iteration times, e.g. the inclinationor slope of the curve shown in FIG. 12A that may be calculated accordingto formulae (3)-(5), the description of which is not repeated here. Or,the feature value may be the difference, or the reciprocal of thedifference, between the predetermined image feature value of thefiltered image obtained from the current iteration and that of thefiltered image obtained from the previous iteration, or may be any otherappropriate feature values. Then, the stopping determination unit 803-3determines whether a predetermined relation between the calculatedfeature value and the predetermined threshold is met, for example, inthe case that the inclination, slope or difference is used as thefeature value, the stopping determination unit 803-3 may determinewhether the feature value is smaller than or equal to a predeterminedthreshold, or in the case that the reciprocal of the difference is usedas the feature value the stopping determination unit 803-3 may determinewhether the feature value is greater than or equal to a predeterminedthreshold. If the predetermined relation between the calculated featurevalue and the predetermined threshold is met, the stopping determinationunit 803-3 indicates the filter 801 to stop the iterative filtering,otherwise, indicates the filter 801 to perform the next iteration. As inthe above described embodiment/example, the above mentioned thresholdmay be predetermined according to the actual application scenario, forexample, according to the variation curve of the image feature valueshown in FIG. 12A. Such curves can be obtained by analyzing images ofdifferent types (or features). The value of the threshold may bedifferent in different application scenarios or may be different whendifferent feature parameters are adopted, and should not be limited toany specific value.

An embodiment of determining a filtering stopping time using thevariation speed of a predetermined image feature value of the noiseimage is described below by reference to FIGS. 9 and 11.

As shown in FIG. 9, the image processing apparatus 900 may include afilter 901 and an iterative filtering stopping device 903. Similar tothe embodiment shown in FIG. 8, the iterative filtering stopping device903 includes an image feature calculation unit 903-4 and a stoppingdetermination unit 903-3. Different from the embodiment shown in FIG. 8,the iterative filtering stopping device 903 may further include a noiseimage acquisition unit 903-1. The image processing apparatus 900 canperform an image filtering by using the method shown in FIG. 11.

Similar to the aforementioned filters 101 and 301, the filter 901 isconfigured to perform an iterative filtering (Step 1102 shown in FIG.11) on an input image input to the image processing apparatus, thedescription of which is not repeated here.

The filter 901 outputs the filtered image obtained from each iterationto the noise image acquisition unit 903-1. The noise image acquisitionunit 903-1 may evaluate, after each iteration, the noise image accordingto the obtained filtered image and the input image (Step 1106-1 shown inFIG. 11) by using the method described in FIG. 3 or 4, the descriptionof which is not repeated here.

The noise image acquisition unit 903-1 outputs the acquired noise imageto the image feature calculation unit 903-4. The image featurecalculation unit 903-4 may calculate a predetermined image feature valueof the noise image (Step 1106-2 shown in FIG. 11), such as the varianceor standard deviation of the noise image, and outputs the result of thecalculation to the stopping determination unit 903-3.

The stopping determination unit 903-3 may determine whether to stop theiterative filtering according to the variation speed of the imagefeature value of the noise image (e.g. the image feature may be thevariance or standard deviation of the noise image, and in this case thevariation speed may refer to the increasing speed of the variance orstandard deviation) with the increase of iteration times during theiterative filtering process. If determining the variation speed of theimage feature value of the noise image reaches a predetermined level,the stopping determination unit 903-3 indicates the filter 901 to stopthe iterative filtering, otherwise, indicates the filter 901 to performthe next iteration.

As a specific example, the stopping determination unit 903-3 maycalculate a feature value representing the variation speed of thepredetermined image feature value during the iterative filtering processaccording to the calculation result of the image feature calculationunit 903-4. For example, the feature value may be the inclination orslope of a curve (e.g. the inclination or slope of the curve shown inFIG. 12B that may be calculated according to formulae (3)-(5), thedescription of which is not repeated here) reflecting the variation ofthe predetermined image feature value with the increase of iterationtimes, or the feature value may be the difference (or the reciprocal ofthe difference) between the predetermined image feature value of thenoise image obtained from the current filtering iteration and that ofthe noise image obtained from the previous filtering iteration, or otherappropriate feature value which is not numerated herein. Then, thestopping determination unit 903-3 determines whether a predeterminedrelation between the calculated feature value and a predeterminedthreshold is met, for example, in the case that the inclination, slopeor difference is used as the feature value, the stopping determinationunit 903-3 may determine whether the feature value is smaller than orequal to the predetermined threshold; and in the case that thereciprocal of the difference is used as the feature value, the stoppingdetermination unit 903-3 may determine whether the feature value isgreater than or equal to the predetermined threshold. If thepredetermined relation between the calculated feature value and thepredetermined threshold is met, the stopping determination unit 903-3indicates the filter 901 to stop the iterative filtering, otherwise,indicates the filter 901 to perform the next iteration. Similar to theabove described embodiment/example, the threshold here may bepredetermined according to the actual application scenario. The value ofthe threshold may be different in different application scenarios orwhen different feature parameters are adopted. For example, thethreshold can be predetermined according to the variation curve of theimage feature value shown in FIG. 12B, wherein such curves can beobtained by analyzing images of different types (or features). Thethreshold is not limited to any specific value.

In the image processing apparatus and method described above withreference to FIGS. 8-11, by using the variation speed of a predeterminedimage feature value of the filtered image or noise image during theiterative filtering process, the iterative filtering stopping time canbe effectively determined. In addition, useful information in the imageis guaranteed not to be smoothed while most noise is filtered out fromthe image.

As a specific example, the image processing apparatus and methoddescribed above with reference to FIGS. 8-11 may calculate the varianceof an image according to the following formula:

D=√{square root over (E[I−E(I)]²)}  (6)

wherein I represents an image (e.g. the above-mentioned filtered imageor noise image), and D represents the variance of the image.

FIG. 13 is a block diagram illustrating the structure of an imageprocessing apparatus according to another specific embodiment of thedisclosure, and FIG. 14 is a schematic diagram illustrating the flow ofan image processing method according to the specific embodiment of theFIG. 3. In the embodiments that have been described above, thecorrelation between a noise image and a filtered image or the variationspeed of a predetermined image feature value of the noise image orfiltered image during the iterative filtering process is utilized, whilein the embodiment shown in FIGS. 13 and 14, the combination of the twoparameters is used. As shown in FIGS. 6A, 6B and 6C, the correlationbetween the noise image and the filtered image decreases monotonicallyat the initial stage of an iterative filtering process and then ascendsslowly (as shown in FIGS. 6A and 6B) or continues to decrease slowly (asshown in FIG. 6C), whereas the variance of the noise image ascendsmonotonically during the iterative filtering process, as shown in FIG.12B. If the two parameters are appropriately combined (a specificcombination embodiment is described below by reference to FIGS. 13-15),then the curve reflecting the variation of the generated combinedfeature value with the increase of iteration times will also reflect thevariation of the image during the iterative filtering processing. Forexample, in the case where the trough of the correlation curve isdelayed after the correct filtering stop time as shown in FIG. 6B, sincethe variance of the noise image ascends monotonically, the curve of thecombined feature value will cause the trough to reach an appropriateposition which is earlier than the trough shown in FIG. 6B. For anotherexample, in the case where the correlation decreases monotonically asshown in FIG. 6C, since the variance of the noise image ascendsmonotonically, the curve of the combined feature value will present atrough which corresponds to the correct stopping time. FIG. 15A is aschematic diagram illustrating the variations of the correctioncoefficient between the noise image and the filtered image, the varianceof the noise image and the combined feature value of the two parameters.In FIG. 15A, the vertical axis represents the values of the correlationcoefficient, the variance and the combined feature, and the horizontalaxis represents iteration time. As show in FIG. 15A, the curve of thecombined feature represents a trough (the minimal value) at time pointt=0.4. An iterative filtering stopping time can be determined accordingto the trough (minimal value) presented by the combined feature valuewith the increasing of iteration times.

For concise, in the example of the following description, the varianceof the noise image is used as the predetermined image feature and thecorrelation coefficient is used to represent the correlation between thenoise image and a filtered image. It should be appreciated that in otherexamples, other image features (e.g. standard deviation) of the noiseimage, or the reciprocal of the variance or standard deviation of thefiltered image or the like may be used as the predetermined imagefeature, and other appropriate parameters that reflects the correlationbetween the filtered image and the noise image can be adopted, which arenot numerated here.

As shown in FIG. 13, an image processing apparatus 1300 may include afilter 1301 and an iterative filtering stopping device 1303. Theiterative filtering stopping device 1303 includes a noise imageacquisition unit 1303-1, a correlation acquisition unit 1303-2, an imagefeature calculation unit 1303-4, a combination unit 1303-5 and astopping determination unit 1303-3. The image processing apparatus 1300can perform an image filtering by using the method shown in FIG. 14.

Similar to the filters 101 and 301, the filter 1301 is configured toperform an iterative filtering (Step 1402 shown in FIG. 14) on an inputimage input to the image processing apparatus, the description of whichis not repeated here.

The filter 1301 outputs the filtered image obtained from each iterationto the noise image acquisition unit 1303-1. The noise image acquisitionunit 1303-1 evaluates, after each iteration, a noise image according tothe obtained filtered image and the input image (Step 1406-1 shown inFIG. 14) using the method described in FIGS. 3 and 4, the description ofwhich is not repeated here.

The noise image acquisition unit 1303-1 outputs the obtained noise imageto the correlation acquisition unit 1303-2 and the image featurecalculation unit 1303-4. The correlation acquisition unit 1303-2estimates the correlation between the noise image and the filtered imageobtained from the current filtering iteration (Step 1406-2 shown in FIG.14) and outputs the result of the estimation to the combined unit1303-5. For example, the correlation acquisition unit 1303-2 maycalculate the correlation coefficient between the noise image and thefiltered image (using the above-mentioned method, the description ofwhich is not repeated here) to represent the correlation between the twoimages.

The image feature calculation unit 1303-4 calculates a predeterminedimage feature value of the noise image (Step 1406-4 shown in FIG. 14),for example, the variance of the noise image (using formula (6)), andoutputs the result of the calculation to the combination unit 1303-5.

The combination unit 1303-5 combines the correlation value acquired bythe correlation acquisition unit 1303-2 and the variance valuecalculated by the image feature calculation unit 1303-4 so as togenerate a combined feature value (Step 1406-4 shown in FIG. 14). Forinstance, supposing C₁ represents the monotonically-ascending variancecurve of the noise image and C₂ represents the correlation curve of thenoise image and the filtered image, the combined feature value may berepresented as follows:

C _(combine) =f(C ₁)+g(C ₂)  (7)

wherein C_(combine) represents the combined feature value, f()represents any mathematical variation performed on the curve C₁, andg() represents any mathematical variation performed on the curve C₂.

As an example, the combination unit 1303-5 may calculate the sum of thecorrelation value acquired by the correlation acquisition unit 1303-2and the variance value calculated by the image feature calculation unit1303-4, as the combined feature value. However, in some cases, the valuerange of the correlation coefficient between the filtered image and thenoise image is different from that of the variance of the noise image,therefore, there may be no trough (minimal value) presented by thesummation curve of the correlation value and the variance value,accordingly an adjustment is needed to match the correlation coefficientcurve with the variance curve. Below is the description on some examplesof combining the correlation value acquired by the correlationacquisition unit 1303-2 with the variance value calculated by the imagefeature calculation unit 1303-4.

As an example, an image processing apparatus (e.g. filter 1301) maynormalize the original input image before performing a filtering, inother words, there may be a step of normalizing the input image (notshown) before Step 1402 shown in FIG. 14. Then, the noise imageacquisition unit 1303-1 estimates, after each iteration, the noise imagebased on the normalized input image and the filtered image. Forinstance, the combination unit 1303-5 may calculate the sum of thecorrelation value obtained using the normalized input image (obtainedfrom the correlation acquisition unit 1303-2) and the variance value(obtained from the image feature calculation unit 1303-4), as thecombined feature value.

As another example, the image feature calculation unit 1303-4 maynormalize, after each iteration, the noise image estimated by the noiseimage acquisition unit 1303-1 (the original input images need nonormalization here), calculate the variance of the normalized noiseimage, and output the calculated variance to the combination unit1303-5. The normalization may be realized by mapping the dynamic range(for example, the dynamic range of the image may be represented by[N_(min), N_(max)]) of the noise image to [0, 1]. For input images ofdifferent types, the dynamic range of the noise image resulting fromeach iteration is different, thus, the normalization is not performedbased on the same dynamic range. In this case, a standard dynamic rangemay be designated. In each iteration, the standard dynamic range may beutilized to map the dynamic range of the noise image to [0, 1]. Forinstance, the standard dynamic range of input images of a certain typemay be set to be [S_(min), S_(max)] (it should be appreciated thatdifferent standard dynamic ranges can be adopted for different inputimages). The following formula (8) may be used for the normalizationcalculation:

$\begin{matrix}{N^{\prime} = {\frac{N}{S_{\max} - S_{\min}} + S_{\min}}} & (8)\end{matrix}$

wherein N represents the pixel value of a noise image, and N′ representsa normalized pixel value.

It should be appreciated that the standard dynamic range may bedetermined as needed and should not limited to any specific examples ofthe disclosure. For instance, in the case where the input image is amedical image, the standard dynamic range may be determined according tothe type of the medical diagnostic apparatus acquiring the input imageor according to the scanning protocol of the input image and so on.

In an example, the correlation acquisition unit 1303-2 may calculate thecorrelation based on the noise image (rather than the normalized noiseimage) estimated by the noise image acquisition unit 1303-1.

As another example, the combination unit 1303-5 may convert thecalculated variance of the noise image, and then calculate the sum ofthe converted variance and the correlation value acquired by thecorrelation acquisition unit 1303-2, as the combined feature value. Forinstance, the combination unit 1303-5 may perform a conversion on thevariance of the noise image calculated by the image feature calculationunit 1303-4, and calculate the sum of the converted variance value andthe correlation value acquired by the correlation acquisition unit1303-2, as the combined feature value. For instance, the combinationunit 1303-5 may convert the variance of the noise image calculated bythe image feature calculation unit 1303-4 according to the followingformula:

V _(i) ′=a _(i)(√{square root over (V _(i))}−offset)  (9)

wherein V_(i) represents the variance of the noise image calculated bythe image feature calculation unit 1303-4, V_(i)′ represents theconverted value resulting from the conversion performed by thecombination unit 1303-5 on the variance V_(i), i represents iterationtimes and is equal to 1, 2, . . . ; offset represents a translationcoefficient and is equal to √{square root over (V_(i))}, V_(i)represents the variance of the noise image calculated by the imagefeature calculation unit 1303-4 after the first iteration, a_(i)represents a coefficient which is greater than 1 and increases with theincreasing of the iteration times, for example, a_(i)=1.0+c×i, and crepresents a predetermined constant greater than 0. In formula (9), theconverted variance V_(i)′ is increased by calculating the square root ofthe variance V_(i), the converted variance curve is translated using thetranslation coefficient offset so that the translated curve begins from0. The increasing speed of the converted variance curve is enlarged byusing the coefficient a_(i). Additionally, the increasing speed of theconverted variance curve becomes larger with the increasing of theiteration times. FIG. 15B is a schematic diagram showing the variationsof the converted variance of the noise image, the correlationcoefficient between the noise image and the filtered and the sum of theconverted variance and the correlation coefficient with iteration times.As shown in FIG. 15B, the trough of the obtained summation curve occursearlier than that of the correlation coefficient curve. It is shown byexperiments that, if the trough of the correlation coefficient curveshown in FIG. 15B is taken as the iterative filtering stopping time, theuseful information in the output image after the iterative filtering isblurred, and if the trough of the summation curve is taken as theiterative filtering stopping time, the useful information in theoriginal input image is remained in the output image after the iterativefiltering, while most noise is filtered out.

Furthermore, the parameter C in formula (9) is a value predeterminedaccording to the actual application scenario, for example, it may be anexperimental value or empirical value resulting from an analysis onimages of different types (or features). It can be known from formula(9) that the smaller the parameter C is, the flatter the convertedvariance curve is, and on the contrary, the greater the parameter C is,the higher the ascending speed of the converted variance curve is. Thevalue of the parameter C should be prevented from being too small,otherwise, the variation curve of the combined feature value obtained bythe combination 1303-5 will descend monotonically with the increase ofthe iteration times and will never present a minimal value; on the otherhand, the value of the parameter C should also be prevented from beingtoo large, otherwise, the variation curve of the combined feature valueobtained by the combination 1303-5 will ascend too fast with theincrease of the iteration times, which may result in that the trough ofthe curve occurs earlier than the appropriate iterative filteringstopping time. Therefore, during an analysis on images of differenttypes (or features), the optimal value of the parameter C may bedetermined according to different converted variance curves resultingfrom different values of the parameter C such that the summation curveof the converted variance curve and the correlation curve will present atrough at a position corresponding to an appropriate iteration stoppingtime point. The value of parameter C is different in differentapplication scenarios, and should not be limited to any specific value.

In practical application the value of parameter C may be predeterminedaccording to the type (or characteristics) of images. The same type ofimages (e.g. the same type of imaging device or scanning protocol) maycorrespond to parameter C of the same value. Different types of imagesmay corresponds to different values of parameter C. As a specificexample, a plurality of C values that are different from each other maybe predetermined for different types of images and stored (in the formof a configuration file) in a storage unit (which may be built in theimage processing apparatus or located outside and may be accessible tothe image processing apparatus). In this way, in the actual imageprocessing various types of images may be processed without any manualintervention, which greatly improves the degree of automation of imageprocessing.

As another specific example, the combination unit 1303-5 may convert thevariance calculated by the image feature calculation unit 1303-4according to the following formula:

V _(i) ′=α×V _(i)  (9a)

wherein V_(i) represents the variance of the noise image calculated bythe image feature calculation unit 1303-4, V_(i)′ represents a convertedvalue resulting from a conversion performed by the combination unit1303-5 on the variance V_(i), and α represents the conversion parameter.α may be a value predetermined according to the actual applicationscenario, for example, an experimental value or empirical valueresulting from an analysis on images of different types (or features).During analysis on images of different types (or features), the optimalα value may be determined according to different converted variancecurves resulted by different α values such that the summation curve ofthe converted variance curve and the correlation curve will present atrough at a position corresponding to an appropriate iteration stoppingtime point. The value of α is not limited to any particular value here.In practical application, for images of each type (or characteristics),the value of α may be predetermined. The same α value corresponds to theimages of the same type (e.g. the images generated by the same type ofimaging device or generated according to the same type of scanningprotocol), different α values correspond to images of different types.

The combination unit 1303-5 outputs the obtained combined feature valueto the stopping determination unit 1303-3. The stopping determinationunit 1303-3 may indicate the filer 1301 to stop the iterative filtering(Step 1406-3) when the combined feature value reaches the minimal value(the trough) during the iterative filtering process. Specifically, thestopping determination unit 1303-3 determines whether the combinedfeature value reaches the minimal value, if so, indicates the filter1301 to stop the iterative filtering, otherwise, indicates the filter1301 to perform the next iteration. The stopping determination unit1303-3 may determine whether the combined feature value reaches thetrough (minimal value) by using any appropriate method, for example, thestopping determination unit 1303-3 may compare the combined featurevalue resulting from the current filtering iteration with that resultingfrom the previous filtering iteration, and if the former is greater thanthe latter, the stopping determination unit 1303-3 may determine thecombined feature value is in an ascending trend, and then stop theiterative filtering. Certainly, the stopping determination unit 1303-3may determine whether the combined feature value reaches the trough byusing other appropriate methods that are not listed herein.

In the image processing apparatus or method described above withreference to FIGS. 13-15, an iterative filtering stopping time can beeffectively determined by using the combined feature value obtained bycombining the correlation coefficient between the filtered image and thenoise image and a predetermined image feature value of a noise image. Inaddition, the useful information in the input image is guaranteed not tobe smoothed while most noise is filtered out.

Some embodiments of the disclosure further provide an image processingapparatus or method for performing an iterative filtering using anon-linear diffusion filtering method.

A non-linear diffusion filtering method was proposed by P. Perona and J.Malik in their paper ‘Scale-Space and Edge Detection Using AnisotropicDiffusion’ (Proceedings of IEEE, November 1987, Computer SocietyWorkshop on Computer Vision, pp. 16-22) (referred to as related document2 hereinafter). It should be noted that although the method proposed inrelated document 2 is termed as “anisotropic diffusion”, the method isin fact a non-linear diffusion, rather than a real anisotropicdiffusion. Nonetheless, some papers in this art and other documentsstill call the method of the related document 2 as anisotropicdiffusion. In related document 2, the following equation (named P-Mequation) is proposed:

$\begin{matrix}{\frac{\partial I}{\partial t} = {\nabla\left( {g*{\nabla I}} \right)}} & (10)\end{matrix}$

wherein I represents an image, and g represents a diffusion coefficientwhich is the function of an image gradient |∇I|;

$\begin{matrix}{{g\left( {{\nabla I}} \right)} = \frac{1}{1 + \left( {{{\nabla I}}/K} \right)^{2}}} & (11)\end{matrix}$

wherein K is referred to as a gradient threshold which is a parameterfor controlling the sensitivity to the edge of an image.

The embodiment shown in FIGS. 16-17 provides an image processingapparatus and method for determining, by using the gradient distributionof an image, a gradient threshold K for use in a non-linear diffusionfiltering, wherein FIG. 16 is a block diagram illustrating the structureof an image processing apparatus according to the embodiment, and FIG.17 is a flow chart illustrating the flow of an image processing methodaccording to the embodiment.

As shown in FIG. 16, an image processing apparatus 1600 may include afilter 1601, a gradient threshold determination device 1605 and aniterative filtering stopping device 1603. The image processing apparatus1600 can perform an image filtering by using the method shown in FIG.17.

The filter 1601 is configured to perform an iterative filtering on aninput image (step 1702 shown in FIG. 17) based on a non-linear diffusionfiltering method and output the filtered image obtained from eachiteration to the iterative filtering stopping device 1603. The filter1601 may perform a filtering by using any non-linear diffusion filteringalgorithm that is not limited here.

The iterative filtering stopping device 1603 is configured to determinewhether to stop an iterative filtering according to the variation speedof the filtered image with the increase of iteration times during thefiltering process (Step 1706 shown in FIG. 17). Specifically, theiterative filtering stopping device 1603 may have the similar functionsand structure as the iterative filtering stopping devices 103, 303, 803,903 or 1303 described in aforementioned embodiments/examples (e.g. thefunction of determining an iterative filtering stopping time using themethods illustrated in FIGS. 2-15), the description of which is notrepeated here.

The gradient threshold determination device 1605 is configured tocalculate, before the filter 1601 performs a filtering, the gradient ofeach image unit in an input image or the filtered image obtained fromthe previous filtering (Step 1701 shown in FIG. 17), and determine agradient threshold according to the gradient distribution of all theimage units in the image. The gradient of an image unit refers to thevariation value of the gray level of the image unit compared with theimage units in its neighboring domain.

An image unit may be a pixel in an image, for example, a pixel in atwo-dimensional image or a voxel in a three-dimensional image. FIG. 18shows an example of the gradient distribution of each image unit in animage, in which the horizontal axis represents the gradient value of animage unit, and the vertical axis represents the number of the imageunits corresponding to a gradient value. As an example, the gradientthreshold determination unit 1605 may acquire a proportion valuecorresponding to an input image, and calculate a gradient threshold Kaccording to the proportion value and the gradient distribution suchthat the ratio of the number of the image units having a gradient valuesmaller than the gradient threshold K to the number of all the imageunits in the image is approximately equal to the proportion value. Ofcourse, the proportion can be subjected to any other mathematicalvariations so as to, for example, make the proportion reflect the ratioof the number of the image units having a gradient value smaller thanthe gradient threshold K to the number of the image units having agradient value no smaller than the gradient threshold K.

As a specific example, the gradient values of the image units may besorted in an ascending order or a descending order, and the gradientvalue with a rank corresponding to the proportion value may bedetermined as a gradient threshold such that the ratio of the number ofthe image units having a gradient value smaller than the gradientthreshold K to the number of all the image units in the image isapproximately equal to the proportion value. As another specificexample, the histogram of the gradients of the image units in the imagemay be calculated, and a gradient threshold may be determined accordingto the histogram such that the ratio of the number of the image unitshaving a gradient value smaller than the gradient threshold K to thenumber of all the image units in the image is approximately equal to theproportion value.

The proportion value may be a value that is predetermined according tothe type or characteristics (e.g. the imaging device or scanningprotocol of a medical image) of the input image. The proportion valuemay be represented with P, wherein P is greater than 0 and smallerthan 1. The same type of images may correspond to the same proportionvalue, and different types of images may correspond to differentproportion values. It should be appreciated that in actual applicationscenarios, a corresponding proportion value may be predetermined for thesame type of images by analyzing a plurality of images of this type andthe predetermined proportion value may be used in the subsequentnon-linear diffusion filtering. For instance, a plurality ofpredetermined proportion values each of which corresponds to each of aplurality of types of images may be stored in a storage unit, forexample, in the form of configuration file. The storage unit may bebuilt in the image processing apparatus or located outside andaccessible to the image processing apparatus. In an actual non-lineardiffusion filtering, the gradient threshold determination device 1605may acquire a stored proportion value corresponding to the type orcharacteristics (the type of an imaging device or scanning protocol) ofan input image from the storage unit. In this way, the parameter needsnot to be set manually, which improves the automation of imageprocessing. As being different in different application scenarios, theproportion value is not specifically limited here.

The gradient threshold determination device 1605 may calculate thegradient value of an image unit in an image by using any appropriatemethod and determine the gradient threshold according to the gradientdistribution of all the image units in the image. As an example,supposing the image is an n-dimensional image (n≧1), the gradient valueof an image unit may be calculated according to the following formula:

|I _(grad)(D ₁ , D ₂ , . . . , D _(n))|=√{square root over (I _(D) ₁ ²+I _(D) ₂ ² + . . . I _(D) _(n) ²)}  (12)

wherein D₁, D₂, . . . , D_(n) represents n dimensions of an image,|I_(grad)(D₁, D₂, . . . , D_(n))| represents the gradient value of apixel of an image located at (D₁, D₂, . . . , D_(n)), and I_(D) _(k)(1≦k≦n) represents the derivative of the pixel in the D_(k)th dimension.

In each iteration, the gradient threshold determination device 1605outputs the calculated gradient threshold to the filter 1601, and thefilter 1601 performs a non-linear diffusion filtering (Step 1702 shownin FIG. 17) according to the gradient threshold determined by thegradient threshold determination device 1605.

The image processing apparatus and method according to the embodimentsof the disclosure are applicable to the filtering processing of varioustypes of one-dimensional or multi-dimensional (e.g. two-dimensional orthree-dimensional) images. For example, the image processing apparatusand method are applicable to the noise reduction of medical images, e.g.images formed by the data obtained by a medical diagnostic apparatus. Asan example, each step of the aforementioned method and each moduleand/or unit of the aforementioned apparatus may be implemented bysoftware, firmware and hardware in a medical diagnostic apparatus (e.g.X-ray diagnostic apparatus, UL diagnostic apparatus, CT apparatus, MRIdiagnostic apparatus or PET apparatus) or the combination thereof andserve as one part of the medical diagnostic apparatus. As an example,the method and/or apparatus disclosed in this invention can be appliedto existing medical diagnostic apparatuses after some modifications aremade on some component(s) of existing medical diagnostic apparatuses. Asanother example, each step of the aforementioned method and each moduleand/or unit of the aforementioned apparatus may be implemented by adevice independent from the medical diagnostic apparatus. Each moduleand unit of the aforementioned apparatus is configured via software,firmware, hardware or the combination thereof by a specific means or ina way that is well known by the skilled in this art, the description ofwhich is not repeated here.

As an example, each step of the aforementioned method and each moduleand/or unit of the aforementioned apparatus may be implemented bysoftware, firmware, hardware or the combination thereof. In the casewhere the steps or modules and/or units are implemented by software orfirmware, a software program for realizing the method may be installedon a computer having a dedicated hardware structure (e.g. the generalcomputer 1900 shown in FIG. 19) from a storage medium or network,wherein the computer is capable of implementing various functions wheninstalled with various programs.

In FIG. 19, an operation processing unit (namely, a CPU) 1901 implementsvarious processing via a program stored in a read-only memory or aprogram that is loaded on a random access memory (RAM) 1903 from astorage part 1908. The data required for the various processing of theCPU 1902 is stored in the RAM 1903 as needed. The CPU 1901, the ROM 1902and the RAM 1903 are linked with each other via a bus 1904. Aninput/output interface 1905 may also be linked on the bus 1904.

The following components are linked with the input/output interface1905: an input part 1906 (including keyboard, mouse and the like), anoutput part 1907 (including display (e.g. cathode-ray tub, liquidcrystal display (LCD) and the like), loudspeaker and the like), a memorypart 1908 (including hard disc and the like), and a communication part1909 (including network interface card (e.g. LAN card, modem and thelike). The communication part 1909 implements a communication processingvia a network (e.g. Internet) and may be linked with the input/outputinterface 1905 as needed. A detachable medium 1911, such as magneticdisc, compact disc, magneto optical disc, semiconductor memory and thelike, is mounted on a drive 1910 as needed such that the computerprogram read from the detachable medium 1911 can be installed on thememory part 1908 as needed.

In the case where the aforementioned series of processing is realized bya piece of software, programs constituting the piece of software isinstalled from a network, for example, Internet, or a storage medium,for example, the detachable medium 1911.

Those skilled in the art should understand that the storage medium isnot limited to the detachable medium 1911 shown in FIG. 19 which isdistributed separated from a device so as to provide a user with aprogram and in which programs are stored. The detachable medium 1911 maybe, for example, magnetic disc (including floppy disc (registeredtrademark)), compact disc (including compact disc read-only memory(CD-ROM) and digital video disc (DVD), magneto optical disc (includingmini disc (MD) (registered trademark))), and semiconductor memory. Orthe storage medium may be the hard disc included in the ROM 1902 or thestorage part 1908, and programs are stored in the storage medium and canbe distributed to users along with the storage medium.

The disclosure further discloses a program product in whichmachine-readable instruction codes are stored. And the aforementionedmethod can be implemented when the instruction codes are read andexecuted by a machine.

Accordingly, a storage medium for bearing the program product in whichmachine-readable instruction codes are stored is also included in thedisclosure, the storage medium including but not limited to floppy disc,compact disc, magneto optical disc, memory card, memory stick and thelike.

In the description above on specific embodiments of the disclosure, thefeatures described and/or shown for an embodiment may be used in one ormore other embodiments in the same or similar way, or combined withthose of the other embodiments, or replace those of the otherembodiments.

It should be emphasized that the term ‘include/comprise’ used hereinrefers to the existence of a feature, an element, a step or a component,but not exclusive of the existence or addition of one or more otherfeatures, elements, steps or components.

In the above-mentioned embodiments and examples, appended drawingreference signs consisting of figures are used for representing stepsand/or units. It should be understood by one of ordinary skill in theart that these appended drawing reference signs are only used forfacilitating description and drawing but not for indicating a limitationon order or any other aspects.

In addition, the method disclosed in the disclosure may be implementedsequentially, synchronously or independently in accordance with othertime sequences, not limited to the time sequence described herein.Therefore, the implementation order of the method described herein isnot limitation to the technical scope of the disclosure.

Although the disclosure is described in connection with the embodimentsthereof, it should be understood that all the above-mentionedembodiments and examples are exemplary but not limitative. Variousmodifications, improvements or equivalents can be devised by thoseskilled in the art without departing from the spirit and scope ofappended claims, with the understanding that and those modifications,improvements or equivalents belong to the protection scope of thedisclosure.

1. An image processing apparatus, comprising: a filter for performing aniterative filtering on an input image; and an iterative filteringstopping device for determining whether to stop the iterative filteringaccording to a variation speed of a filtered image obtained from eachiteration of the iterative filtering with increasing of iteration times.2. The image processing apparatus according to claim 1, wherein theiterative filtering stopping device comprises: a noise image acquisitionunit for evaluating, after the filter completes each iteration, a noiseimage according to the filtered image obtained from the iteration andthe input image; a correlation acquisition unit for evaluating acorrelation between the noise image and the filtered image; and astopping determination unit for determining whether to stop theiterative filtering according to a decreasing speed of the correlationduring the iterative filtering.
 3. The image processing apparatusaccording to claim 2, wherein the stopping determination unit isconfigured to: calculate a feature value representing the decreasingspeed of the correlation during the iterative filtering process,determine whether a predetermined relation is met between the featurevalue and a predetermined threshold, and stop the iterative filtering ifthe predetermined relation is met between the feature value and thepredetermined threshold.
 4. The image processing apparatus according toclaim 3, wherein the correlation acquisition unit is configured tocalculate a correlation coefficient between the noise image and thefiltered image to represent the correlation.
 5. The image processingapparatus according to claim 4, wherein the feature value is aninclination value of a curve reflecting decreasing of the correlationcoefficient with the increasing of iteration times during the iterativefiltering.
 6. The image processing apparatus according to claim 3,wherein the predetermined threshold is a value predetermined accordingto characteristics of the input image.
 7. The image processing apparatusaccording to claim 1, wherein the iterative filtering stopping devicecomprises: an image feature calculation unit for calculating apredetermined image feature value of the filtered image after eachiteration; and a stopping determination unit for determining whether tostop the iterative filtering according to a variation speed of thepredetermined image feature value with the increasing of iterationtimes.
 8. The image processing apparatus according to claim 7, whereinthe iterative filtering stopping device further comprises: a noise imageacquisition unit for evaluating, after the filter completes eachiteration, a noise image according to the filtered image obtained fromthe iteration and the input image, wherein the image feature calculationunit is configured to calculate a predetermined image feature value ofthe noise image to reflect the predetermined image feature of thefiltered image.
 9. The image processing apparatus according to claim 7,wherein the predetermined image feature is the variance or standarddeviation of an image.
 10. The image processing apparatus according toclaim 7, wherein the stopping determination unit is configured to:calculate a feature value representing a variation speed of thepredetermined image feature during the iterative filtering, determinewhether a predetermined relation is met between the feature value and apredetermined threshold, and stop the iterative filtering if thepredetermined relation is met between the feature value and thepredetermined threshold.
 11. The image processing apparatus according toclaim 10, wherein the feature value is an inclination value of a curvereflecting variation of the predetermined image feature with theincreasing of iteration times during the iterative filtering.
 12. Theimage processing apparatus according to claim 2, wherein the iterativefiltering stopping device further comprises: an image featurecalculation unit for calculating a predetermined image feature value ofthe noise image; and a combination unit for combining the correlationand the predetermined image feature value of the noise image to obtain acombined feature value, wherein the stopping determination unit isconfigured to stop the iterative filtering when the combined featurevalue is minimum during the iterative filtering.
 13. The imageprocessing apparatus according to claim 12, wherein the predeterminedimage feature is variance or standard deviation of an image.
 14. Theimage processing apparatus according to claim 12, wherein the imagefeature calculation unit is further configured to perform a conversionon the calculated predetermined image feature value of the noise imageto obtain a converted predetermined image feature value, and thecombination unit is configured to calculate the sum of the correlationand the converted predetermined image feature value, as the combinedfeature value.
 15. The image processing apparatus according to claim 12,wherein the correlation acquisition unit is configured to calculate acorrelation coefficient between the noise image and the filtered imageto represent the correlation.
 16. The image processing apparatusaccording to claim 1, wherein the filter is configured to perform thefiltering by using a non-linear diffusion method.
 17. The imageprocessing apparatus according to claim 16, further comprising: agradient threshold determination device for calculating, before thefilter performs each iteration, a gradient value of each image unit inthe input image or the filtered image obtained from a previousiteration, and determining a gradient threshold according to gradientdistribution of all image units in the image, wherein the filter isconfigured to perform, by using a non-linear diffusion method, eachfiltering iteration using the gradient threshold determined by thegradient threshold determination device.
 18. The image processingapparatus according to claim 17, wherein the gradient thresholddetermination device is configured to: acquire a proportion valuecorresponding to the input image and calculate the gradient thresholdaccording to the gradient distribution and the proportion value, a ratioof number of image units having a gradient value smaller than thegradient threshold in the image to number of all image units in theimage being approximately equal to the proportion value.
 19. The imageprocessing apparatus according to claim 18, wherein the proportion valueis a value predetermined according to characteristics of the inputimage.
 20. The image processing apparatus according to claim 1, whereinthe input image is a medical image formed by data obtained by a medicaldiagnostic apparatus.
 21. An image processing method, comprising:performing an iterative filtering on an input image; and determiningwhether to stop the iterative filtering according to a variation speedof a filtered image obtained from each iteration of the iterativefiltering with increasing of iteration times.
 22. A non-transitorycomputer readable storage medium having stored therein a program forcausing a computer to execute a process, comprising: performing aniterative filtering on an input image; and determining whether to stopthe iterative filtering according to a variation speed of a filteredimage obtained from each iteration of the iterative filtering withincreasing of iteration times.